AMLE graph value extensions meet a local action-gap certificate guaranteeing goal-reaching greedy rollouts under argmin-Q planning and achieve 0.97 success on AntMaze-derived graphs versus 0.58 for harmonic extension.
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SGC-RML creates an 8D symptom atlas from multimodal PD data and integrates conformal calibration to deliver reliable, rejectable longitudinal assessments.
Develops a skew-adaptive split conformal prediction method that learns local skewness via a gauge-derived conformity score and an asinh residual model while preserving marginal validity under exchangeability.
RareCP improves interval efficiency for time series conformal prediction by retrieving and weighting regime-specific calibration examples while adapting to drift and maintaining coverage.
A new conformal framework learns polyhedral uncertainty sets tailored to robust optimization objectives, minimizing decision loss while preserving coverage via calibration and independent re-calibration.
TA-CQR adaptively allocates miscoverage tails to produce shortest single-interval conformal prediction sets with exact marginal coverage and provides oracle inequalities for length.
Context Kubernetes formalizes six abstractions for knowledge orchestration in agentic AI, with experiments showing a three-tier permission model blocks all five tested attack scenarios where simpler baselines fail.
A data-driven method adaptively selects the number of LLM-simulated responses to form confidence sets with nominal coverage for human survey parameters and equates that number to the LLM's effective human-equivalent sample size.
Bayesian deep learning method rankings are unstable at small sample sizes, dataset-dependent, and require uncertainty-aware evaluation using hierarchical models and minimum detectable difference curves.
CONFIDE applies conformal prediction to transformer embeddings for valid prediction sets, improving accuracy up to 4.09% and efficiency over baselines on models like BERT-tiny.
citing papers explorer
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Planner-Admissible Graph-PDE Value Extensions for Sparse Goal-Conditioned Planning
AMLE graph value extensions meet a local action-gap certificate guaranteeing goal-reaching greedy rollouts under argmin-Q planning and achieve 0.97 success on AntMaze-derived graphs versus 0.58 for harmonic extension.
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SGC-RML: A reliable and interpretable longitudinal assessment for PD in real-world DNS
SGC-RML creates an 8D symptom atlas from multimodal PD data and integrates conformal calibration to deliver reliable, rejectable longitudinal assessments.
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Skew-adaptive conformal prediction
Develops a skew-adaptive split conformal prediction method that learns local skewness via a gauge-derived conformity score and an asinh residual model while preserving marginal validity under exchangeability.
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RareCP: Regime-Aware Retrieval for Efficient Conformal Prediction
RareCP improves interval efficiency for time series conformal prediction by retrieving and weighting regime-specific calibration examples while adapting to drift and maintaining coverage.
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Learning Polyhedral Conformal Sets for Robust Optimization
A new conformal framework learns polyhedral uncertainty sets tailored to robust optimization objectives, minimizing decision loss while preserving coverage via calibration and independent re-calibration.
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Tail allocation for conformal prediction intervals
TA-CQR adaptively allocates miscoverage tails to produce shortest single-interval conformal prediction sets with exact marginal coverage and provides oracle inequalities for length.
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Context Kubernetes: Declarative Orchestration of Enterprise Knowledge for Agentic AI Systems
Context Kubernetes formalizes six abstractions for knowledge orchestration in agentic AI, with experiments showing a three-tier permission model blocks all five tested attack scenarios where simpler baselines fail.
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How Many Human Survey Respondents is a Large Language Model Worth? An Uncertainty Quantification Perspective
A data-driven method adaptively selects the number of LLM-simulated responses to form confidence sets with nominal coverage for human survey parameters and equates that number to the LLM's effective human-equivalent sample size.
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Unstable Rankings in Bayesian Deep Learning Evaluation
Bayesian deep learning method rankings are unstable at small sample sizes, dataset-dependent, and require uncertainty-aware evaluation using hierarchical models and minimum detectable difference curves.
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Uncertainty-Aware Transformers: Conformal Prediction for Language Models
CONFIDE applies conformal prediction to transformer embeddings for valid prediction sets, improving accuracy up to 4.09% and efficiency over baselines on models like BERT-tiny.